We also observe the variation in refinement across datasets. From the density plots in Figure REF  we can note the depleting joint concentration between correct-incorrect samples from (a) to (c). The plots indicate that the refinement is less pronounced for CIFAR-10 than CIFAR-100 and ImageNet. As a result, the lowering of predicted confidence has a much more drastic effect on refinement for CIFAR-10 than on the other datasets. To further add to this observation we noticed that the training accuracy achieved over CIFAR-10, CIFAR-100 and ImageNet are \(99.9\%,\, 99.1\%, \, \text{and} \, 77.6\%\) . [1]} hypothesize the miscalibration of the baseline due to over-fitting caused during the training. The comparatively lower training accuracy for ImageNet would suggest that the baseline is not severely over-fitted and hence displays a relatively lower degradation in terms of refinement.
